Delving into the specifics of spatial and temporal analytics, this chapter explores topics such as spatial neighborhood and temporal evolution of large amounts of network traffic data.

Focusing on the big data elements of cybersecurity, this chapter looks at the landscape of the big data technologies and the complexities of the different types of data, including spatial and graph data. It outlines examples in these complex data types and how they can be evaluated using data analytics.

The goal of this chapter is to provide an overview of how environmental sociologists can use spatial data and analytical techniques to advance environmental sociology. This chapter begins with the premise that individuals are embedded within specific environmental contexts and, consequently, spatial data and analyses are tools that help identify environmental forces relevant to human society. We assert that the environment is inherently spatial, and that the explicit consideration of one location relative to another is a distinguishing feature of “spatial” studies. This chapter begins with an overview of general definitions, and foundational theoretical and methodological concepts. We then highlight compelling spatially-explicit work in the environmental sociology literature on migration, land use, environmental justice, sustainable livelihoods, and poverty. Finally, we conclude with a discussion of future possibilities to enhance theories on human–environment interactions by incorporating spatial data. Our overarching aim is to elucidate the relational nature between locations, the environment, and human-environment processes in order to encourage the use of spatial tools and to promote new ways of thinking spatially.

This concluding chapter discusses the strengths and limitations of Hierarchical Modelling of Species Communities (HMSC) in light of the results presented in this book. Concerning the strengths, the chapter notes that HMSC is a unifying framework that encompasses classic approaches such as single-species distribution models and model-based ordinations as special cases, which hence provides simultaneous inferences at the species and community levels. As another key strength, the chapter notes that HMSC can be applied to many kinds of study designs (including hierarchical, temporal or spatial) and many types of data (such as presence–absence, counts and continuous measurements). The chapter further emphasises that HMSC offers the general advantages of model-based approaches, such as tools for model validation and prediction, and that it is especially well suited for predictive modelling of communities with sparse data. Concerning the limitations, the chapter discusses three areas where future development is needed: a broader set of data models, a broader array of model structures related to various ecological and evolutionary processes, and improved computational efficiency.

This chapter describes the types of data that empirical community ecologists typically collect, and how these can be incorporated in the Hierarchical Modelling of Species Communities (HMSC) framework as input. While community ecologists apply theoretical, experimental and observational approaches to studying the processes that structure ecological communities, this chapter (and the entire book) focuses mainly on empirical research based on non-manipulative observational data. Understanding the basic features of the data and how they have been collected will be essential for appropriately setting up the HMSC model and interpreting the results. The chapter describes each type of input HMSC data, namely the community data (i.e. the occurrences or abundances of the species), environmental data, data describing the spatio-temporal context, species trait data and phylogenetic data. Finally, the chapter discusses how to best organise the data, as well as how to solve problems arising from missing data.

This chapter applies Hierarchical Modelling of Species Communities (HMSC) to a real dataset on Finnish birds, with the aim of using the case study to simultaneously demonstrate the many uses of HMSC. Specifically, it illustrates the full workflow of a typical HMSC analysis, shows how the researcher can access the full posterior distribution to go beyond the default outputs of HMSC analyses, shows how predictions of HMSC can be used as a starting point for further analyses as well as compares HMSC outputs to results obtained by other statistical methods in community ecology. The chapter starts by outlining the five steps of the HMSC workflow, and then shows how the researcher can access the entire posterior distribution of model parameters or predictions, e.g. for examining the level of statistical support related to either of these. Next the chapter illustrates how one may use HMSC predictions as a starting point for applied research, such as spatial conservation prioritisation or bioregionalisation. Finally, the chapter applies other widely used methods in statistical community ecology such as ordination methods and co-occurrence analysis to the same data, with the aim of comparing how their results relate to those obtained by HMSC.

This chapter

• Presents the basic concepts, terms and definitions pertaining to spatial analysis

• Introduces a spatial analysis workflow that follows a Describe–Explore–Explain structure

• Presents in detail the reasons why spatial data are special, namely spatial autocorrelation, scale, the modifiable area unit problem, spatial heterogeneity, the edge effects and the ecological fallacy

• Explains why conceptualization of spatial relationships is extremely important in spatial analysis

• Presents the approaches used to conceptualize spatial relationships

• Explains how distance, contiguity/adjacency, neighborhood, proximity polygons and space–time window are used in space conceptualization

• Defines the spatial weights matrix, which is essential to almost every spatial statistic/ technique

• Introduces the real-world project along with the related dataset to be worked throughout the book

After a thorough study of the theory and lab sections, you will be able to

• Implement a comprehensive workflow when you conduct spatial analysis

• Distinguish spatial from nonspatial data

• Understand why spatial data should be treated with new methods (e.g., spatial statistics)

• Understand the importance of applying conceptualization methods according to the problem at hand

• Understand essential terms for conducting spatial analysis as for example distance, contiguity/adjacency, neighborhood, proximity polygons and space–time

• Describe the spatial analysis process to be adopted for solving the real-world project of this book

• Presents the project’s data with ArcGIS and GeoDa

This chapter examines the links between Hierarchical Modelling of Species Communities (HMSC) outputs and the underlying community ecological processes. To do so, the chapter applies HMSC to simulated data generated from an agent-based model with known underlying assembly processes, and then assesses how those processes are captured from the patterns in the data. After simulating data with the spatial agent-based model, the chapter simulates two 'virtual ecologists' who sample data from the simulations, one applying a spatial study design and the other a temporal study design. While the main motivation of the chapter is to assess how community assembly processes translate into HMSC outputs, another motivation is to examine the robustness of HSMC to violations against structural model assumptions – namely, the data generated by the agent-based models violate some of the underlying assumptions of generalised linear mixed models and thus of HMSC. The chapter finishes by summarising what the virtual ecologists learned by applying HMSC to their data, particularly in light of the assembly processes that were used to simulate the data.